The Latent Dirichlet Allocation (LDA) topic model accepts data in the following format (choose one best answer):

Question

BlackTom AI · Accepted Answer

The question asks which data format is accepted by the Latent Dirichlet Allocation (LDA) topic model, choosing the best option.
Option 1: Bag-of-words. In LDA implementations, documents are typically represented as a bag-of-words (BoW) vector or a word-count distribution over a fixed vocabulary, where the order of words is ignored and only counts or frequencies are used. This representation aligns with the generative assumptions of LDA, where each document is a mixture of topics and each topic is a distribution over words.
Option 2: Raw text. While raw text is the original input source, LDA does not operate directly on raw strings without conversion. Before LDA can be applied, the text must be tokenized and converted into a BoW-like representation (or another numerical feature representation). Thus, raw text is not the format LDA directly accepts as input.
In summary, LDA expects a BoW-style representation (counts or probabilities over a fixed vocabulary) rather than plain raw text, because the model relies on discrete word-vocabulary statistics to infer topic distributions.

SP2025.B69.DAT.562.24 COMPLETE Quiz #4 AFTER VIDEO 3: Latent Dirichlet Allocation (LDA)

The Latent Dirichlet Allocation (LDA) topic model accepts data in the following format (choose one best answer):

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类似问题

The output of the Latent Dirichlet Allocation (LDA) topic model is:

The Latent Dirichlet Allocation (LDA) topic model is a generative model, that is it mimics (but in a simplified way) how humans write documents:

Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

The Latent Dirichlet Allocation (LDA) topic model needs an initial guess for the distribution of topics in documents and distribution of words in each topic:

The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

The Latent Dirichlet Allocation (LDA) approach to topic modeling assumes that:

The Latent Dirichlet Allocation (LDA) estimates the distribution of topics in documents and the distribution of words in topics

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